Modelling anisotropic phenomena of friction of deep-drawing quality steel sheets using artificial neural networks

• Autorzy: Trzepieciński Tomasz , Lemu Hirpa G. , Chodoła Łukasz , Ficek Daniel , Szczęsny Ireneusz

Identyfikatory

DOI

10.2478/adms-2021-0016

• Języki publikacji: EN

Abstrakty

EN

This paper presents a method of determining the coefficient of friction in metal forming using multilayer perceptron based on experimental data obtained from the pin-on-disk tribometer. As test material, deep-drawing quality DC01, DC03 and DC05 steel sheets were used. The experimental results show that the coefficient of friction depends on the measured angle from the rolling direction and corresponds to the surface topography. The number of input variables of the artificial neural network was optimized using genetic algorithms. In this process, surface parameters of the sheet, sheet material parameters, friction conditions and pressure force were used as input parameters to train the artificial neural network. Some of the obtained results have pointed out that genetic algorithm can successfully be applied to optimize the training set. The trained multilayer perceptron predicted the value of the friction coefficient for the DC04 sheet. It was found that the tested steel sheet exhibits anisotropic tribological properties. The highest values of the coefficient of friction under dry friction conditions were registered for sheet DC05, which had the lowest value of the yield stress. Prediction results of coefficient of friction by multilayer perceptron were in qualitative and quantitative agreement with the experimental ones.

Słowa kluczowe

EN

artificial neural networks; coefficient of friction; genetic algorithm; friction; sheet metal forming

PL

sztuczne sieci neuronowe; współczynnik tarcia; algorytm genetyczny; tarcie; formowanie blachy

• Wydawca: Politechnika Gdańska
• Czasopismo: Advances in Materials Science
• Rocznik: 2021
• Tom: Vol. 21, nr 3(69)
• Strony: 31--42
• Opis fizyczny: Bibliogr. 28 poz., tab., il., zdj., wykr.

Twórcy

autor

Trzepieciński Tomasz

• Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Department of Materials Forming and Processing, Rzeszów, Poland

autor

Lemu Hirpa G.

• University of Stavanger, Department of Mechanical and Structural Engineering, Stavanger, Norway

autor

Chodoła Łukasz

• Rzeszow University of Technology, Faculty of Mechanics and Technology, Department of Integrated Design and Tribology Systems, Stalowa Wola, Poland

autor

Ficek Daniel

• Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Department of Aerospace Engineering, Rzeszów, Poland

autor

Szczęsny Ireneusz

• Rzeszow University of Technology, Faculty of Mechanical Engineering and Aeronautics, Department of Aerospace Engineering, Rzeszów, Poland

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).